Intelligent control method and system for vehicle-mounted atmosphere lamp and storage medium

Abstract

The invention relates to the technical field of vehicle control, and discloses an intelligent control method and system for a vehicle-mounted atmosphere lamp and a storage medium. The method comprises the following steps: acquiring external environment data, in-vehicle member state data and vehicle operation data through a data acquisition module; a hue setting module determines an initial hue path based on the member state score and a hue policy; the brightness setting module determines initial brightness in combination with member age types, external environment data and a brightness mapping table; the frequency setting module sets an initial frequency according to vehicle working conditions and a frequency mapping table; and the atmosphere lamp control module generates a control instruction according to the initial hue path, the brightness and the frequency, and controls the atmosphere lamp based on the control instruction, thereby improving intelligence, personalization and scenario of atmosphere lamp control, and improving user experience.

Description

Technical Field

[0001] This application relates to the technical field of vehicle control, and particularly to an intelligent control method, system, and storage medium for in-vehicle ambient lights. Background Art

[0002] With the continuous development of automotive technology, in-vehicle ambient lights, as an important configuration to enhance the driving and riding experience, have gradually received attention. Traditional in-vehicle ambient lights are mainly controlled manually or through preset modes, and cannot be intelligently adjusted according to the actual state of the occupants and environmental changes. In recent years, with the development of artificial intelligence, sensor technology, and machine learning, it has become possible to achieve intelligent control of in-vehicle ambient lights.

[0003] A similar prior art is the Chinese patent application with the publication number CN119497281A, which discloses an intelligent control method, device, electronic device, and storage medium for vehicle ambient lights. It acquires temperature data obtained by collecting the external environmental temperature of the vehicle using multiple temperature sensors; preprocesses the temperature data to obtain the comprehensive external temperature of the vehicle; based on the comprehensive external temperature of the vehicle, determines the corresponding color value and brightness value of the ambient light using a preset mapping relationship; transmits the corresponding color value and brightness value of the ambient light to the ambient light controller, so that the ambient light controller adjusts the color and brightness of the ambient light according to the color value and brightness value to respond to the change of the external environmental temperature of the vehicle. This method controls the ambient light based on the external environment, does not reflect the needs of people, and has a low degree of personalization. There is also the Chinese patent application with the publication number CN119364604A, which discloses a control method and control system for in-vehicle ambient lights. It acquires image data about the vehicle occupants through an image acquisition device arranged inside the vehicle, and obtains the basic information of the vehicle occupants according to the image data; inputs the basic information into a trained prediction model to obtain the preference information of the vehicle occupants, and the preference information is the respective color preferences of the vehicle occupants; uses an algorithm to obtain specified color information according to the preference information, and the specified color information includes multiple colors determined by RGB values; generates a lighting control instruction according to the specified color information through cloud service and sends it to the vehicle's central control system, so that the in-vehicle ambient light is adjusted according to the age and gender distribution of the vehicle occupants. This method controls the ambient light based on the static characteristics and preferences of the occupants, and lacks dynamic response to the real-time state of the vehicle occupants.

[0004] Therefore, it is an urgent problem to be solved to provide an intelligent control method, system, and storage medium for in-vehicle ambient lights to achieve automatic, personalized, and scenario-based adjustment of the ambient lights, and improve the driving and riding experience and comfort. Summary of the Invention

[0005] This application provides an intelligent control method, system, and storage medium for in-vehicle ambient lights.

[0006] In a first aspect, the present application provides an intelligent control method for an in-vehicle ambient light. The method includes: Extract a data acquisition module for acquiring external environment data, first user status data of vehicle occupants, and vehicle operation data; Provide a hue setting module for analyzing the first user status data to identify the first status score of each member, determining a hue change path based on the status recognition result and a preset hue strategy, and setting the hue change path as the initial hue path; Provide a brightness setting module for analyzing the first user status data to identify the age type of each member, and determining the initial brightness based on the age recognition result, external environment data, and a preset brightness mapping table; Provide a frequency setting mode for analyzing the vehicle operation data to identify the vehicle working condition, and determining the initial frequency of the hue change path based on the working condition recognition result and a preset frequency mapping table; Provide an ambient light control module for generating a control instruction based on the initial hue path, initial brightness, and initial frequency, and controlling the ambient light based on the control instruction.

[0007] In combination with the first aspect, in the first implementation manner of the first aspect of the present application, the first user status data includes facial images, first physiological data, and second physiological data. Analyzing the first user status data to identify the first status score of each member includes: Extract any member, and analyze the facial image, first physiological data, and second physiological data corresponding to any member respectively to obtain the first member status, the second member status, and the third member status. Among them, the member status includes a status type and a level corresponding to the status type, and the status type includes negative, normal, and positive; Based on a first preset rule table, adjust the first member status by referring to the second member status and the third member status respectively to generate a first reference status and a second reference status; Search for a first status score mapping table, obtain the first score and the second score corresponding to the first reference status and the second reference status respectively, perform a weighted average on the first score and the second score to obtain the first status score corresponding to any member, and after traversing all members, obtain the first status score of each member.

[0008] In combination with the first aspect, in the second implementation manner of the first aspect of the present application, analyzing the facial image to obtain the first member status includes: Perform face pose estimation on the facial image to obtain a first pose angle, perform key point detection on the facial image to obtain first key point information, and detect the first feature vector of each facial organ based on the first key point information; Obtain the standard facial image corresponding to any member, control the standard facial image to rotate based on the first pose angle, obtain the reference facial image, perform key point detection on the reference facial image, obtain the second key point information, and detect the second feature vector of each facial organ based on the second key point information; Compare the first feature vector and the second feature vector, and label each state type based on the comparison result and the second preset rule table. The labeling includes positive labeling and negative labeling; Statistically calculate the labeled net value of each state type based on the labeling result, define the state type corresponding to the maximum labeled net value as the pending state type, and look up the second state score mapping table based on the pending state type and the maximum labeled net value to obtain the first member state.

[0009] Combined with the first aspect, in the third implementation manner of the first aspect of the present application, the standard facial image is a facial image with a state type of normal and a second pose angle of zero, where the second pose angle is the facial pose angle of the standard facial image.

[0010] Combined with the first aspect, in the fourth implementation manner of the first aspect of the present application, based on the state recognition result and the preset hue strategy, determining the hue change path includes: Step 11: Obtain the minimum score and the maximum score in the first state score, and compare the minimum score and the maximum score with the preset state range; Step 12: Determine whether the minimum score is within the first preset range. If not, proceed to step 13. If so, determine that the ambient light control is in the first mode. Then, determine whether the historical hue storage module stores the first hue sequence corresponding to the minimum score. If so, set the first hue sequence as the hue change path. If not, obtain the second hue sequence corresponding to the minimum score based on the first mode and the hue strategy, and set the second hue sequence as the hue change path; Step 13: Determine whether the maximum score is within the second preset range. If not, proceed to step 14. If so, determine that the ambient light control is in the first mode. Then, based on the first mode and the hue strategy, set the third hue sequence corresponding to the maximum score as the hue change path; Step 14: Determine that the ambient light control is in the second mode, obtain the display image of the vehicle center control display screen, extract the main color other than black in the display image, and based on the second mode and the hue strategy, set the fourth hue sequence corresponding to the main color as the hue change path.

[0011] Combined with the first aspect, in the fifth implementation manner of the first aspect of the present application, an ambient light adjustment module is further provided. When the minimum score is within the first preset range, the ambient light is adjusted, including: Step 121: After the ambient light operates according to the control instruction for a preset time, obtain the second user status data of the member corresponding to the minimum score, and analyze the second user status data to obtain the second status score; Step 122: Determine whether the second status score is within the third preset range. If not, proceed to step 123. If so, continue to control the ambient light based on the control instruction, and simultaneously store the hue change path included in the control instruction corresponding to the minimum score in the historical hue storage module, where the third preset range is between the first preset range and the second preset range; Step 123: Calculate the difference between the second status score and the minimum score, defined as the adjustment value. Adjust the initial hue path, initial brightness, and initial frequency based on the adjustment value to generate a new control instruction, and control the ambient light based on the new control instruction, then return to step 121.

[0012] Combined with the first aspect, in the sixth implementation manner of the first aspect of the present application, the first user status data includes facial images. Analyze the first user status data to identify the age types of each member, including: Step 21: Extract any member, perform operations on the facial image of any member to obtain multiple skin appearance feature images, where the skin appearance feature image is an image that retains any appearance feature and eliminates other appearance features; Step 22: Extract any skin appearance feature image, define the appearance feature of any skin appearance feature image as the first appearance feature, extract the representative image corresponding to the first appearance feature, compare any skin appearance feature image with the representative image, and make a positive adjustment or a reverse adjustment to the age stage corresponding to the representative image according to the comparison result, and extract a new representative image based on the adjusted age stage. Repeat step 22 until the number of reverse adjustment points is greater than or equal to the preset value or the age stage tends to be stable. Calculate the estimated age corresponding to the first appearance feature based on the age stage at the reverse adjustment point, where the reverse adjustment point is the point when the adjustment direction changes; Step 23: After calculating the estimated ages corresponding to all appearance features, perform a weighted average on all estimated ages to obtain the final estimated age of any member, and obtain the age type of any member based on the final estimated age and the preset age type mapping table.

[0013] Combined with the first aspect, in the seventh implementation manner of the first aspect of the present application, determining the initial brightness based on the age recognition result, external environment data, and the preset brightness mapping table includes: Step 31: Determine whether there is a member with an age type of the first preset type. If not, proceed to step 32. If so, based on the brightness mapping table, determine the initial brightness by referring to the first preset type and the external environment data respectively; Step 32: Determine whether there is a member whose age type is the second preset type. If not, proceed to Step 33. If so, based on the brightness mapping table, determine the initial brightness by referring to the second preset type and the external environment data respectively. Step 33: Based on the brightness mapping table, determine the initial brightness by referring to the third preset type and the external environment data.

[0014] In a second aspect, the present application provides an intelligent control system for an in-vehicle atmosphere lamp, and the system includes: A data acquisition module, configured to acquire external environment data, first user status data of vehicle occupants, and vehicle operation data; A hue setting module, configured to analyze the first user status data, identify the first status score of each member, determine the hue change path based on the status recognition result and a preset hue strategy, and set the hue change path as the initial hue path; A brightness setting module, configured to analyze the first user status data, identify the age type of each member, and determine the initial brightness based on the age recognition result, the external environment data, and a preset brightness mapping table; A frequency setting mode, configured to analyze the vehicle operation data, identify the vehicle working condition, and determine the initial frequency of the hue change path based on the working condition recognition result and a preset frequency mapping table; An atmosphere lamp control module, configured to generate a control instruction according to the initial hue path, the initial brightness, and the initial frequency, and control the atmosphere lamp based on the control instruction.

[0015] In a third aspect of the present application, a computer-readable storage medium is provided. Instructions are stored in the computer-readable storage medium. When the instructions run on a computer, the computer is caused to execute the above-mentioned intelligent control method for an in-vehicle atmosphere lamp.

[0016] Compared with the prior art, the beneficial effects of the technical solution of the present application are at least as follows: 1. The data acquisition module acquires external environment data, first user status data of vehicle occupants, and vehicle operation data, and in combination with the hue setting module, the brightness setting module, and the frequency setting module, dynamically adjusts the hue, brightness, and frequency of the atmosphere lamp respectively. Through multi-dimensional data fusion, it can comprehensively sense the states of the driver and passengers, provide the most suitable lighting effects for the driver and passengers in different scenarios, make the control of the atmosphere lamp more intelligent and personalized, and improve the comfort and experience of the driver and passengers.

[0017] 2. By analyzing the first user status data of the vehicle occupants, the emotional states and age types of each occupant can be accurately identified, and corresponding hue change paths, brightness, and frequencies can be generated based on preset strategies. Through personalized adjustment methods, different occupants' states and needs can be responded to, improving the psychological comfort and driving safety of the occupants.

[0018] 3. Through dynamic adjustment and adaptive learning, the hue, brightness, and frequency of the ambient light are corrected and optimized according to the real-time feedback of user status data to provide an ambient light setting that better meets the user's needs. The optimized parameters are stored in the historical hue storage module, which can better adapt to different users' needs and scenario changes, further improving the system's intelligence level and user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0020] Figure 1 It is a schematic diagram of an embodiment of an intelligent control method for an in-vehicle ambient light in an embodiment of the present application; Figure 2 It is a schematic diagram of an embodiment of a method for determining a hue change path in an embodiment of the present application; Figure 3 It is a schematic diagram of an embodiment of an ambient light adjustment method in an embodiment of the present application; Figure 4 It is a schematic diagram of an embodiment of an intelligent control system for an in-vehicle ambient light in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] The embodiments of the present application provide an intelligent control method, system, and storage medium for an in-vehicle ambient light. The terms "first", "second", "third", "fourth", etc. (if any) in the specification, claims, and drawings of the present application are used to distinguish similar objects and do not necessarily describe a specific order or sequence. It should be understood that such used data can be interchanged under appropriate circumstances so that the embodiments described here can be implemented in an order different from that illustrated or described here. In addition, the terms "comprising" or "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those clearly listed steps or units, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0022] For ease of understanding, the specific process of the embodiments of this application will be described below. Please refer to Figure 1 One embodiment of an intelligent control method for an in-vehicle atmosphere lamp in the embodiments of this application includes: Extract a data acquisition module for acquiring external environment data, first user status data of vehicle occupants, and vehicle operation data.

[0023] Specifically, external environment data, first user status data of vehicle occupants, and vehicle operation data are collected in real time through various sensors and devices (such as cameras, light sensors, wearable devices, vehicle sensors, etc.). Among them, the external environment data includes light intensity, time (day, night), etc., the first user status data includes person images, tones, heart rates, pulses, etc., and the vehicle operation data includes vehicle speed, acceleration, steering angle, etc.

[0024] Provide a hue setting module for analyzing the first user status data, identifying the first status score of each member, determining the hue change path based on the status recognition result and a preset hue strategy, and setting the hue change path as the initial hue path.

[0025] Specifically, hue describes the basic type of color, which is the position of the color on the color wheel and is usually represented by an angle or a value. Common color wheel designs include the 12-hue color wheel, 24-hue color wheel, etc. Taking the 12-hue color wheel as an example, it has 3 primary colors: red, yellow, and blue, 3 secondary colors: orange (red + yellow), green (yellow + blue), purple (red + blue), and 6 tertiary colors: red-orange, yellow-orange, yellow-green, blue-green, blue-violet, red-violet. These hues together form a complete color wheel, and the transition between adjacent colors on the color wheel is natural without obvious boundaries.

[0026] Specifically, by analyzing the first user status data of vehicle occupants and analyzing the status score of each member, their current emotional state and corresponding level are determined. These states are usually classified into types such as negative, normal, and positive. For example, a score higher than a certain threshold indicates a positive state, and a score lower than a certain threshold indicates a negative state. The hue strategy is a preset rule set that defines the hue change path corresponding to different user status scores, usually formulated based on psychological and color theory principles. For example, in a positive state, the system may select warm colors (such as red, orange, yellow) to create a warm atmosphere; in a negative state, it may select cool colors (such as blue, purple, green) to provide a soothing effect; in a normal state, neutral colors (such as white, light yellow) are selected.

[0027] Generate an initial hue change path according to the recognized status type and the preset hue strategy. For example, it is possible to choose to fade from one color to another or cycle through several colors, so that the in-vehicle ambient light can provide a more comfortable visual environment that better meets the emotional needs of users. This personalized lighting adjustment can effectively relieve fatigue and improve mood, while also having an entertainment effect, further enhancing the in-vehicle atmosphere and driving experience.

[0028] Provide a brightness setting module for analyzing the first user status data, identifying the age type of each member, and determining the initial brightness based on the age recognition result, external environment data, and the preset brightness mapping table.

[0029] Exemplarily, based on the age, the age types of the members are set as children, teenagers, adults, the elderly, etc. Passengers of different age groups have different brightness requirements. For example, children may be more sensitive to high brightness, and a lower brightness is needed to reduce irritation; while the elderly may need a higher brightness to ensure visual clarity. At the same time, the external light intensity directly affects the brightness required inside the vehicle. For example, higher brightness is needed during the day or in strong light environments to ensure the visibility of the lights, while lower brightness is required at night or in low light conditions to avoid glare to the driver and reduce visual interference.

[0030] The brightness mapping table is a pre-set mapping relationship table that maps the age type and external environment data to specific brightness values. For example, when there is a child in the vehicle at night, the brightness is set to a lower value (such as 20%); when all the members in the vehicle are adults during the day, the brightness is set to a higher value (such as 60%). By dynamically adjusting the ambient light brightness according to the age type of the in-vehicle members and the external environment, a comfortable visual experience can be provided for the passengers, reducing eye fatigue and discomfort.

[0031] Provide a frequency setting mode for analyzing the vehicle operation data, identifying the vehicle working conditions, and determining the initial frequency of the hue change path based on the working condition recognition result and the preset frequency mapping table.

[0032] Specifically, the vehicle working conditions include low-speed conditions, normal conditions, high-speed conditions, etc.

[0033] When the vehicle is traveling at high speed, if the flashing frequency of the ambient light is too low, the flashing light may form a visual disharmony with the rapid movement of the vehicle, causing the driver and passengers to feel dizzy or uncomfortable; moreover, the low-frequency flashing may be perceived as a "jumping" light in a high-speed scenario, which is likely to distract the driver's attention and even cause visual fatigue. When traveling at low speed, if the flashing frequency of the ambient light is too high, it may exceed the comfortable perception range of the human eye, resulting in an overly strong "flashing sensation" visually; at the same time, the too-high flashing frequency may cause a sense of dizziness, especially when the people in the vehicle are relatively stationary, and this visual stimulus will be more obvious. Therefore, when traveling at low speed (0 - 30 km / h): the flashing frequency should be maintained within a relatively low but stable range (for example, flashing 1 - 2 times per second) to avoid strong visual stimuli; when traveling at medium speed (30 - 60 km / h): the flashing frequency can be appropriately increased (for example, flashing 2 - 3 times per second) to enhance driving feedback while avoiding visual fatigue; when traveling at high speed (above 60 km / h): the flashing frequency should be further increased (for example, flashing 3 - 5 times per second) to ensure the coordination between the light flashing and the vehicle movement, and at the same time avoid the visual discomfort caused by low-frequency flashing.

[0034] The frequency mapping table is a pre-set rule set that defines the hue change frequencies corresponding to different vehicle working conditions. According to the identified vehicle working condition and in combination with the pre-set frequency mapping table, the flashing frequency of the ambient light that matches the current working condition is determined. The flashing frequency can be increased when traveling at high speed to remind the driver and passengers that the vehicle is accelerating, and decreased when traveling at a constant speed to reduce visual interference, which can effectively avoid the dizziness or visual discomfort caused by the mismatch between the vehicle speed and the flashing frequency, provide a more soothing visual environment, and help enhance the immersion sense of driving, the perception ability of the vehicle state, and safety.

[0035] Provide an ambient light control module for generating control instructions based on the initial hue path, initial brightness, and initial frequency, and controlling the ambient light based on the control instructions.

[0036] The ambient light control module receives the initial parameters from other modules, converts the hue, brightness, and frequency parameters generated by the front-end module into specific light control instructions, and drives the ambient light hardware device to work to achieve the corresponding light effects.

[0037] In a specific embodiment, the first user state data includes facial images, first physiological data, and second physiological data. Analyzing the first user state data to identify the first state scores of each member includes: Extract any member, and analyze the corresponding facial image, first physiological data, and second physiological data of any member respectively to obtain the first member state, the second member state, and the third member state. Among them, the member state includes the state type and the level corresponding to the state type, and the state types include negative, normal, and positive.

[0038] Based on the first preset rule table, adjust the first member state with reference to the second member state and the third member state respectively to generate the first reference state and the second reference state.

[0039] Search for the first state score mapping table, obtain the first score and the second score corresponding to the first reference state and the second reference state respectively, perform a weighted average on the first score and the second score to obtain the first state score corresponding to any member. After traversing all members, obtain the first state score of each member.

[0040] Specifically, facial expressions may be subjective and disguisable, and it is difficult to recognize the true feelings through facial expressions when the facial expressions of members are not rich enough or are more restrained. Physiological signals (such as heart rate, skin conductance, body temperature, etc.) are usually difficult to be subjectively controlled or disguised by individuals and can provide real-time emotional state information. Facial expressions mainly reflect the external emotional expressions of individuals, while physiological signals reflect the internal physiological reactions of individuals. The two provide information on different dimensions of the emotional state. Combining the two can verify and complement each other, thereby improving the accuracy and reliability of emotion recognition.

[0041] Exemplarily, the physiological data includes indicators such as tone, heart rate, pulse, respiratory rate, body temperature, skin conductance, etc. Preferably, the tone is set as the first physiological data, and the pulse is set as the second physiological data.

[0042] The first preset rule table is a set of predefined rules for adjusting the state type and / or the level corresponding to the state type of the first member state according to the second member state or the third member state of any member. Among them, the levels corresponding to negative, normal, and positive include high, medium, and low. The higher the level corresponding to positive or normal, the higher the degree of its positivity or normality, and the higher the level of negativity, the higher the degree of its negativity. Exemplarily, if the state type of the first member state is positive and the level is high, and the state type of the second member state is positive and the level is low, then the adjusted first reference state is the state type is positive and the level is medium; if the state type of the first member state is normal and the level is low, and the state type of the second member state is negative and the level is medium, then the adjusted first reference state is the state type is negative and the level is low.

[0043] The first state score mapping table is used to map the adjusted member state into specific scores, so as to quantify the member state for subsequent weighted average calculation and the formulation of the atmosphere light control strategy. Exemplarily, positive state: score range: 70 - 100 points. Description: It indicates that the member is in a positive and pleasant state; normal state: score range: 40 - 69 points. Description: It indicates that the member is in a calm and neutral state; negative state: score range: 0 - 39 points. Description: It indicates that the member is in a negative, fatigued or stressed state. Based on the above state score division, different state types are further divided into different levels. For example, the levels of the positive state: high is 90 - 100 points, medium is 80 - 89 points, low is 70 - 79 points; the levels of the normal state: high is 60 - 69 points, medium is 50 - 59 points, low is 40 - 49 points; the levels of the negative state: high is 0 - 19 points, medium is 20 - 29 points, low is 30 - 39 points. For simplicity of calculation, a fixed value can be set for each level of each state type respectively. For example, the levels of the positive state: high is 95 points, medium is 85 points, low is 75 points; the levels of the normal state: high is 65 points, medium is 55 points, low is 45 points; the levels of the negative state: high is 10 points, medium is 25 points, low is 35 points. The above example is in a percentile system, and it can also be set as a decimal system, which is specifically set according to the experience of those skilled in the art or according to the actual application scenario, and the embodiments of the present application do not limit this.

[0044] Specifically, different weights can be set according to the reliability, importance and recognition accuracy of different data. A single data source may have deviations due to various factors (such as measurement errors, environmental interference). Through weighted average, not only the importance and reliability of different data sources are considered, but also the deviation of a single data source is reduced through multi-dimensional data fusion, improving the accuracy of state assessment.

[0045] In a specific embodiment, analyzing the facial image to obtain the first member state includes: (1) Perform face pose estimation on the facial image to obtain the first pose angle, perform key point detection on the facial image to obtain the first key point information, and detect the first feature vector of each facial organ based on the first key point information.

[0046] (2) Obtain the standard facial image corresponding to any member, control the standard facial image to rotate based on the first pose angle to obtain the reference facial image, perform key point detection on the reference facial image to obtain the second key point information, and detect the second feature vector of each facial organ based on the second key point information.

[0047] (3) Compare the first feature vector and the second feature vector, and label each state type based on the comparison result and the second preset rule table. The labeling includes positive labeling and negative labeling.

[0048] (4) Based on the annotation results, count the annotation net value of each status type, define the status type corresponding to the maximum annotation net value as the pending status type, and based on the pending status type and the maximum annotation net value, look up the second status score mapping table to obtain the first member status.

[0049] In a specific embodiment, the standard facial image is a facial image with a status type of normal and a second pose angle of zero, where the second pose angle is the facial pose angle of the standard facial image.

[0050] Specifically, when capturing an in-vehicle image, the captured member's facial image is not necessarily the standard front image with the person facing the camera directly. To improve the accuracy of subsequent estimation of the facial status based on the annotated facial image, first calculate the first pose angle of the member to determine the facial orientation of the member. Among them, the annotated facial image is the benchmark for comparison with the current facial image. To eliminate the recognition deviation caused by different face poses and thus improve the accuracy and reliability of emotion status recognition, before performing status recognition, first adjust the pose angle of the standard facial image based on the rotation of the first pose angle to make it consistent with the pose angle of the member's facial image.

[0051] When performing key point detection, detect the key points of each facial organ (eyes, nose, mouth, eyebrows, chin, etc.) respectively. Exemplarily, the detected key points include the inner and outer corners of both eyes, the upper and lower eyelids, the brows, the peaks, and the tails of both eyebrows, the tip of the nose, both nostrils, both corners of the mouth, the midpoint of the lip bow, and the chin, etc. After the key point detection is completed, generate the feature vector of each facial organ respectively, where each element represents a specific measurement value. Taking the eyebrows as an example, the corresponding feature vector is [eyebrow length, eyebrow spacing, eyebrow tilt angle, eyebrow curvature, eyebrow raising degree (vertical distance between the peak and the tail of the eyebrow)]; taking the mouth as an example, the corresponding feature vector is [mouth width, vertical distance of the corner of the mouth relative to the center of the mouth, distance between the upper and lower lips].

[0052] Taking the second feature vector of each facial organ of the reference facial image as a standard, compare the first feature vector of each facial organ of the member facial image with it to analyze the changes of the facial organs, and further label each state type based on the changes and the second preset rule table. The state types include negative, normal, and positive. Exemplarily, for the feature vector corresponding to the eyebrows, if the eyebrow distance decreases, the negative state type is positively labeled as +1, and the normal and positive state types are negatively labeled as -1; if the eyebrow tilt angle decreases, the positive state type is positively labeled as +1, and the negative state type is negatively labeled as -1; if the eyebrow curvature increases, the positive state type is positively labeled as +1, and the negative state type is negatively labeled as -1; if the eyebrow raising degree increases, the positive state type is positively labeled as +1, and the negative state type is negatively labeled as -1; for the feature vector corresponding to the mouth, if the mouth width becomes larger, the positive state type is positively labeled as +1; if the vertical distance from the corner of the mouth to the center of the mouth increases, the positive state type is positively labeled as +1, and the negative state type is negatively labeled as -1; if the distance between the upper and lower lips increases, the positive state type is positively labeled as +1, and the negative state type is negatively labeled as -1.

[0053] The second state score mapping table is a preset reference table for mapping the analyzed net annotation value to a specific state type and further distinguishing the level of the state type based on the size of the net annotation value. Its principle is the same as that of the first state score mapping table and will not be elaborated here.

[0054] By comparing the feature differences between the facial image and the reference image, the changes of the key points of the facial organs are obtained. Further, the possibility of a specific state is quantified through positive and negative labeling, improving the accuracy and precision of facial image state estimation. Moreover, compared with machine learning, this technical solution has strong interpretability and customizability, depends on predefined rules and a small number of feature points, does not require a large amount of training data, and has a small computational load.

[0055] In a specific embodiment, based on the state recognition result and the preset hue strategy, determining the hue change path includes: Step 11: Obtain the minimum score and the maximum score in the first state score, and compare the minimum score and the maximum score with the preset state range.

[0056] Step 12: Determine whether the minimum score is within the first preset range. If not, proceed to Step 13. If so, determine that the ambient light control is in the first mode. Then, check whether the historical hue storage module stores the first hue sequence corresponding to the minimum score. If so, set the first hue sequence as the hue change path. If not, obtain the second hue sequence corresponding to the minimum score based on the first mode and the hue strategy, and set the second hue sequence as the hue change path.

[0057] Step 13: Determine whether the maximum score is within the second preset range. If not, proceed to Step 14. If so, determine that the ambient light control is in the first mode. Then, set the third hue sequence corresponding to the maximum score as the hue change path based on the first mode and the hue strategy.

[0058] Step 14: Determine that the ambient light control is in the second mode. Obtain the display image of the vehicle's center console display screen, extract the dominant color other than black in the display image, and set the fourth hue sequence corresponding to the dominant color as the hue change path based on the second mode and the hue strategy.

[0059] Please refer to Figure 2 , which is a schematic diagram of an embodiment of the method for determining the hue change path in the embodiment of the present application. Specifically, the first state score corresponds to the state type and the level corresponding to the state type. The smaller the score, the more it tends to a negative state, and the larger the score, the more it tends to a positive state. According to the ranges where the minimum score and the maximum score are located, determine which mode the ambient light control should be in (the first mode (set based on members) or the second mode (set based on the vehicle)), and select the corresponding hue sequence as the hue change path to respond to the state of the vehicle occupants.

[0060] Negative states have a great impact on driving safety and ride comfort, and their prevalence and urgency are relatively high. Negative states are given priority. When the minimum score is within the first preset range, it indicates that there are members in a negative state in the vehicle. Then, set the hue sequence (such as blue → purple → green, light blue → pink → off-white, or dark blue → silver gray → light gray, etc.) used to soothe the user's mood as the hue change path to produce a calming effect. The historical hue storage module can record valid hue sequences. If the current state matches the historical state, the historical sequence is directly used to avoid repeated calculations and improve the system efficiency. If there is no matching historical sequence, a new hue sequence is generated according to the preset hue strategy. Among them, there can be multiple historical sequences that match the current state. Preferably, identify the members corresponding to the negative state, and extract the historical sequence corresponding to the member based on the identification result.

[0061] When the maximum score is within the second preset range, it indicates that the members in the vehicle are in a positive state. To respond to the members' emotional states, a cheerful hue sequence (yellow → orange → red, light yellow → pink → lavender, or gold → champagne → white, etc.) is set as the hue change path to enhance the sense of pleasure and vitality.

[0062] The central control display screen is one of the visual focuses in the vehicle, and its display content (such as navigation maps, multimedia information, etc.) has an important impact on the visual experience of the driver and passengers. When the ambient light control is in the second mode, setting the hue change path based on the dominant color other than black in the display image of the central control display screen can achieve the coordination and unity of the visual elements in the vehicle, avoid visual conflicts or disharmony, and help improve the overall driving and riding experience and reduce visual distraction. Exemplarily, when the dominant color of the display image of the central control display screen is green, the hue change path is set as green → yellow - green → light green.

[0063] According to the technical solution of the present invention, dynamically adjusting the hue change path of the ambient light according to the state type of the members in the vehicle realizes a highly intelligent and personalized adjustment method for the ambient light, providing a more comfortable and pleasant vehicle environment for the driver and passengers.

[0064] In a specific embodiment, the method further provides an ambient light adjustment module. When the minimum score is within the first preset range, the ambient light is adjusted, including: Step 121: After the ambient light runs according to the control instruction for a preset time, obtain the second user state data of the member corresponding to the minimum score, and analyze the second user state data to obtain the second state score.

[0065] Step 122: Determine whether the second state score is within the third preset range. If not, go to step 123; if so, continue to control the ambient light based on the control instruction, and at the same time store the hue change path included in the control instruction corresponding to the minimum score in the historical hue storage module, where the third preset range is between the first preset range and the second preset range.

[0066] Step 123: Calculate the difference between the second state score and the minimum score, defined as the adjustment value. Adjust the initial hue path, initial brightness, and initial frequency based on the adjustment value to generate a new control instruction, control the ambient light based on the new control instruction, and then return to step 121.

[0067] Please refer to Figure 3, which is a schematic diagram of an embodiment of the ambient light adjustment method in the present application. Specifically, after the ambient light operates according to the initial control instruction for a period of time, it evaluates whether the lighting effect is effective. The setting of the preset time is to ensure that the lighting adjustment has enough time to affect the mood of the members, and it is set according to the experience of those skilled in the art or according to the actual application scenario. The embodiments of the present application do not limit this. Exemplarily, the preset time is 15 seconds, 30 seconds or 1 minute.

[0068] If the second state score is within the third preset range, it indicates that the member's state has tended to be calm and the current lighting setting is effective. The system stores the current hue path in the historical hue storage module and can be reused in a similar state later to improve the control efficiency.

[0069] If the second state score is not within the third preset range, then further judge the current lighting effect based on the adjustment value, and adjust the initial hue path, initial brightness and initial frequency based on the adjustment value. Exemplarily, if the adjustment value is greater than the preset threshold, it indicates that the current control strategy is effective, and continue to control the ambient light using the current hue path, brightness and frequency; if the adjustment value is less than the preset threshold but greater than 0, it indicates that the current lighting control strategy is effective, but the effect is not sufficient to reach the ideal state. At this time, the brightness and / or frequency can be fine-tuned to enhance the lighting control effect. Taking the brightness adjustment as an example, if the current brightness is low, the brightness can be appropriately increased. If the current brightness is already high, the dynamic range of the brightness can be adjusted so that it gradually changes within a certain time instead of being fixed at a value. If the adjustment value is less than 0, it indicates that the current lighting control strategy is ineffective and may even have a negative impact on mood regulation. At this time, a comprehensive adjustment of the hue path, brightness and frequency is required. For example, if the initial hue path is "green → yellow-green → light green", it can be changed to "blue → dark blue → light blue". If the current brightness is high, the brightness can be appropriately reduced. If the current flashing frequency is high, the flashing frequency can be reduced.

[0070] The technical solution of the present invention provides an ambient light adapted to the state and preferences of the vehicle occupants through dynamic adaptability and personalized adjustment, enhancing the comfort and safety of the occupants. Moreover, through closed-loop control and historical data storage, it can continuously learn and optimize, improving the intelligent level and control efficiency of the system.

[0071] In a specific embodiment, the first user state data includes facial images. Analyzing the first user state data to identify the age type of each member includes: Step 21, extract any member, perform on the facial image of any member to obtain a plurality of skin appearance feature images, where the skin appearance feature image is an image that retains any appearance feature and eliminates other appearance features.

[0072] Step 22: Extract any skin appearance feature image, define the appearance feature of the any skin appearance feature image as the first appearance feature, extract the representative image corresponding to the first appearance feature, compare the any skin appearance feature image with the representative image, positively adjust or negatively adjust the age stage corresponding to the representative image according to the comparison result, and extract a new representative image based on the adjusted age stage. Repeat Step 22 until the number of negative adjustment points is greater than or equal to the preset value or the age stage tends to be stable. Calculate the estimated age corresponding to the first appearance feature based on the age stage at the negative adjustment point, where the negative adjustment point is the point when the adjustment direction changes.

[0073] Step 23: After calculating the estimated ages corresponding to all appearance features, perform a weighted average on all the estimated ages to obtain the final estimated age of any member. Based on the final estimated age and the preset age type mapping table, obtain the age type of any member.

[0074] Specifically, the skin appearance features are wrinkles (nasolabial folds, forehead wrinkles, and / or crow's feet), pigmented spots (senile plaques, freckles, and / or melasma), and / or skin firmness. Through techniques such as adaptive threshold processing, skin appearance feature images that only contain wrinkle features, pigmented spot features, and / or skin firmness features can be obtained from facial images. That is, the skin appearance feature image is an image that eliminates other features and only retains one feature, separating and highlighting one feature while ignoring other interfering features, which helps to more accurately analyze the impact of this feature on age estimation.

[0075] The representative image is obtained by extracting and analyzing from a large amount of sample data and can reflect the typical features of different age stages. Taking the representative image as a reference standard and comparing it with the skin appearance feature image of the in-vehicle member, through the comparison, the similarity between the specific features (such as wrinkles, pigmented spots, and / or skin firmness) of the member's facial image and the standard image can be evaluated, thereby inferring the age type of the member.

[0076] Exemplarily, any skin appearance feature image is extracted and first compared with the representative image corresponding to 30 years old. If the skin appearance feature image is older than the representative image of 30 years old, a positive adjustment is made. Further, the representative image corresponding to 35 years old is extracted. If the positive adjustment continues, when the representative image corresponding to 47 years old is extracted and the skin appearance feature image is younger than the representative image of 47 years old, a negative adjustment is made (at this time, 47 years old is the negative adjustment point), and the representative image corresponding to 45 years old is extracted. If the negative adjustment continues, when the representative image corresponding to 43 years old is extracted and the skin appearance feature image is older than the representative image of 43 years old, a positive adjustment is made (at this time, 43 years old is the positive adjustment point). This process is repeated until the adjustment tends to be stable or reaches a preset condition (such as the number of negative adjustment points reaches a threshold), and the average age at the negative adjustment point is used as the estimated age corresponding to the above appearance feature.

[0077] The weighted average of the estimated ages corresponding to all appearance features is calculated to obtain the final estimated age of the member. Subsequently, based on the final estimated age and in combination with a preset age type mapping table, the age type of the member (such as child, adolescent, adult, elderly) is determined. Among them, the weights can be assigned according to the importance of the features.

[0078] The appearance features of the skin can provide different information about age, but there may be certain errors or limitations when estimating age for each feature alone. By integrating the estimation results of multiple features, the errors caused by a single feature can be reduced, and the age type of the member can be estimated more accurately, improving the overall estimation accuracy.

[0079] In a specific embodiment, determining the initial brightness based on the age recognition result, external environment data, and a preset brightness mapping table includes: Step 31: Determine whether there is a member whose age type is the first preset type. If not, proceed to step 32; if so, based on the brightness mapping table, refer to the first preset type and the external environment data respectively to determine the initial brightness.

[0080] Step 32: Determine whether there is a member whose age type is the second preset type. If not, proceed to step 33; if so, based on the brightness mapping table, refer to the second preset type and the external environment data respectively to determine the initial brightness.

[0081] Step 33: Based on the brightness mapping table, refer to the third preset type and the external environment data to determine the initial brightness.

[0082] Specifically, the members of the first preset type are children, the members of the second preset type are the elderly, and the members of the third preset type are teenagers and adults. The eyes of children are more sensitive to light, and strong light may cause harm to their eyesight. Therefore, it is necessary to prioritize adjusting the brightness of children to ensure that they are in a comfortable and safe lighting level; the eyesight of the elderly usually declines with age, and their ability to adapt to light is relatively weak; strong light may cause visual fatigue or discomfort, so it is necessary to prioritize adjusting the brightness to ensure their comfort; the visual needs of teenagers and adults are relatively common. If there are no children and the elderly in the vehicle, the system will determine the brightness based on the default settings (applicable to teenagers and adults) and combined with external environment data.

[0083] The technical solution of the present invention reflects the attention and care for members with special needs by prioritizing the adjustment of the brightness of children and the elderly. It not only conforms to the principle of humanization but also effectively improves the overall comfort and safety of the vehicle interior. Moreover, it can ensure that the lighting settings can adapt to different environmental conditions, enhance visual comfort and safety, and optimize the user experience.

[0084] The intelligent control method of an in-vehicle atmosphere lamp in the embodiments of the present application has been described above. Next, an intelligent control system of an in-vehicle atmosphere lamp in the embodiments of the present application will be described. Please refer to Figure 4 , an embodiment of an intelligent control system of an in-vehicle atmosphere lamp in the embodiments of the present application includes: A data acquisition module 10 for acquiring external environment data, the first user status data of vehicle occupants, and vehicle operation data.

[0085] A hue setting module 20 for analyzing the first user status data, identifying the first status score of each member, determining the hue change path based on the status recognition result and the preset hue strategy, and setting the hue change path as the initial hue path.

[0086] A brightness setting module 30 for analyzing the first user status data, identifying the age type of each member, and determining the initial brightness based on the age recognition result, external environment data, and the preset brightness mapping table.

[0087] A frequency setting mode 40 for analyzing the vehicle operation data, identifying the vehicle working condition, and determining the initial frequency of the hue change path based on the working condition recognition result and the preset frequency mapping table.

[0088] An atmosphere lamp control module 50 for generating a control instruction according to the initial hue path, the initial brightness, and the initial frequency, and controlling the atmosphere lamp based on the control instruction.

[0089] The present application also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions run on a computer, the computer is caused to execute the steps of the intelligent control method for an in-vehicle atmosphere lamp.

[0090] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described system, system, and unit can refer to the corresponding processes in the foregoing method embodiments and will not be described herein again.

[0091] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0092] As described above, the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present application.

Claims

1. An intelligent control method for an in-vehicle atmosphere lamp, characterized in that, The method includes: extracting a data acquisition module for acquiring external environment data, first user status data of in-vehicle members, and vehicle operation data; providing a hue setting module for analyzing the first user status data, identifying the first status score of each member, determining a hue change path based on the status recognition result and a preset hue strategy, and setting the hue change path as the initial hue path; providing a brightness setting module for analyzing the first user status data, identifying the age type of each member, and determining the initial brightness based on the age recognition result, the external environment data, and a preset brightness mapping table; providing a frequency setting mode for analyzing the vehicle operation data, identifying the vehicle working condition, and determining the initial frequency of the hue change path based on the working condition recognition result and a preset frequency mapping table; providing an ambient light control module for generating a control instruction based on the initial hue path, the initial brightness, and the initial frequency, and controlling the ambient light based on the control instruction.

2. The intelligent control method of an in-vehicle atmosphere lamp according to claim 1, characterized in that, The first user status data includes facial images, first physiological data, and second physiological data. Analyzing the first user status data to identify the first status score of each member includes: extracting any member, respectively analyzing the facial image, the first physiological data, and the second physiological data corresponding to any member to obtain a first member status, a second member status, and a third member status, where the member status includes a status type and a level corresponding to the status type, and the status type includes negative, normal, and positive; adjusting the first member status with reference to the second member status and the third member status respectively based on a first preset rule table to generate a first reference status and a second reference status; searching a first status score mapping table to respectively obtain a first score and a second score corresponding to the first reference status and the second reference status, performing a weighted average on the first score and the second score to obtain the first status score corresponding to any member, and after traversing all members, obtaining the first status score of each member.

3. The intelligent control method of an in-vehicle atmosphere lamp according to claim 2, characterized in that, Analyzing the facial image to obtain the first member status includes: performing face pose estimation on the facial image to obtain a first pose angle, performing key point detection on the facial image to obtain first key point information, and detecting a first feature vector of each facial organ based on the first key point information; obtaining a standard facial image corresponding to any member, controlling the standard facial image to rotate based on the first pose angle to obtain a reference facial image, performing key point detection on the reference facial image to obtain second key point information, and detecting a second feature vector of each facial organ based on the second key point information; comparing the first feature vector and the second feature vector, and labeling each status type based on the comparison result and a second preset rule table, where the labeling includes positive labeling and negative labeling; Based on the annotation results, the annotation net value of each of the state types is statistically calculated, and the state type corresponding to the maximum annotation net value is defined as the pending state type. Based on the pending state type and the maximum annotation net value, the second state score mapping table is searched to obtain the first member state.

4. The intelligent control method of an in-vehicle atmosphere lamp according to claim 3, wherein The standard facial image is a facial image when the state type is normal and the second pose angle is zero, where the second pose angle is the facial pose angle of the standard facial image.

5. The intelligent control method of an in-vehicle atmosphere lamp according to claim 1, characterized in that, The determining of the hue change path based on the state recognition result and the preset hue strategy includes: Step 11: Obtain the minimum score and the maximum score in the first state score, and compare the minimum score and the maximum score with the preset state range; Step 12: Determine whether the minimum score is within the first preset range. If not, go to step 13; if so, determine that the atmosphere light control is in the first mode. Then, determine whether the historical hue storage module stores a first hue sequence corresponding to the minimum score. If so, set the first hue sequence as the hue change path; if not, obtain a second hue sequence corresponding to the minimum score based on the first mode and the hue strategy, and set the second hue sequence as the hue change path; Step 13: Determine whether the maximum score is within the second preset range. If not, go to step 14; if so, determine that the atmosphere light control is in the first mode. Then, set the third hue sequence corresponding to the maximum score as the hue change path based on the first mode and the hue strategy; Step 14: Determine that the atmosphere light control is in the second mode. Obtain the display image of the vehicle center control display screen, extract the main color other than black in the display image, and set the fourth hue sequence corresponding to the main color as the hue change path based on the second mode and the hue strategy.

6. The intelligent control method of an in-vehicle atmosphere lamp according to claim 5, characterized in that, An atmosphere light adjustment module is further provided. When the minimum score is within the first preset range, the atmosphere light is adjusted, including: Step 121: After the atmosphere light runs according to the control instruction for a preset time, obtain the second user state data of the member corresponding to the minimum score, and analyze the second user state data to obtain the second state score; Step 122: Determine whether the second state score is within the third preset range. If not, go to step 123; if so, continue to control the atmosphere light based on the control instruction, and at the same time store the hue change path included in the control instruction corresponding to the minimum score in the historical hue storage module, where the third preset range is between the first preset range and the second preset range; Step 123: Calculate the difference between the second state score and the minimum score, which is defined as the adjustment value. Adjust the initial hue path, the initial brightness, and the initial frequency based on the adjustment value to generate a new control instruction, and control the atmosphere light based on the new control instruction. Then, return to step 121.

7. The intelligent control method of an in-vehicle atmosphere lamp according to claim 1, characterized in that, The first user status data includes facial images. Analyzing the first user status data to identify the age types of each member includes: Step 21: Extract any member, perform operations on the facial image of any member to obtain multiple skin appearance feature images, where the skin appearance feature image is an image that retains any one appearance feature and eliminates other appearance features; Step 22: Extract any skin appearance feature image, define the appearance feature of any skin appearance feature image as the first appearance feature, extract the representative image corresponding to the first appearance feature, compare any skin appearance feature image with the representative image, positively or negatively adjust the age stage corresponding to the representative image according to the comparison result, and extract a new representative image based on the adjusted age stage. Repeat step 22 until the number of negative adjustment points is greater than or equal to the preset value or the age stage tends to be stable. Calculate the estimated age corresponding to the first appearance feature based on the age stage at the negative adjustment point, where the negative adjustment point is the point when the adjustment direction changes; Step 23: After calculating the estimated ages corresponding to all appearance features, perform a weighted average on all estimated ages to obtain the final estimated age of any member. Based on the final estimated age and the preset age type mapping table, obtain the age type of any member.

8. An intelligent control method for an in-vehicle atmosphere lamp according to claim 1, characterized in that, Determining the initial brightness based on the age recognition result, the external environment data, and the preset brightness mapping table includes: Step 31: Determine whether there is a member whose age type is the first preset type. If not, proceed to step 32; if so, based on the brightness mapping table, respectively refer to the first preset type and the external environment data to determine the initial brightness; Step 32: Determine whether there is a member whose age type is the second preset type. If not, proceed to step 33; if so, based on the brightness mapping table, respectively refer to the second preset type and the external environment data to determine the initial brightness; Step 33: Based on the brightness mapping table, refer to the third preset type and the external environment data to determine the initial brightness.

9. An intelligent control system for an in-vehicle ambient light, characterized in that, The system includes: A data acquisition module for acquiring external environment data, the first user status data of vehicle occupants, and vehicle operation data; A hue setting module for analyzing the first user status data to identify the first status score of each member, determining the hue change path based on the status recognition result and the preset hue strategy, and setting the hue change path as the initial hue path; A brightness setting module for analyzing the first user status data to identify the age type of each member, and determining the initial brightness based on the age recognition result, the external environment data, and the preset brightness mapping table; A frequency setting mode for analyzing the vehicle operation data to identify the vehicle working conditions, and determining the initial frequency of the hue change path based on the working condition recognition result and the preset frequency mapping table; The ambient light control module is used to generate a control instruction according to the initial hue path, the initial brightness, and the initial frequency, and control the ambient light based on the control instruction.

10. A computer-readable storage medium, on which instructions are stored, characterized in that, When the instruction is executed by a processor, it implements an intelligent control method for an in-vehicle ambient light as described in any one of claims 1-8.

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